• topic 1
• topic 2
• topic 3

• bullet 1
• bullet 2

In fact, up until recently, the Statistical Atlas had been published and released for each Census since 1870! A large compilation of data visualizaitons based on census data:

1920 Statistical Atlas

• getting api key

All of the tidyverse packages operate easily when you have data in this structure!

Three interrelated rules:

1. Each variable must have its own column.
2. Each observation must have its own row.
3. Each value must have its own cell.

https://r4ds.had.co.nz/tidy-data.html#fig:tidy-structure

library(tidyverse)

## Registered S3 methods overwritten by 'ggplot2':
##   method         from 
##   [.quosures     rlang
##   c.quosures     rlang
##   print.quosures rlang

## ── Attaching packages ─────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

## ✔ ggplot2 3.1.1     ✔ purrr   0.3.2
## ✔ tibble  2.1.1     ✔ dplyr   0.8.1
## ✔ tidyr   0.8.3     ✔ stringr 1.4.0
## ✔ readr   1.3.1     ✔ forcats 0.4.0

## ── Conflicts ────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

library(sf)

## Linking to GEOS 3.7.2, GDAL 2.4.1, PROJ 6.0.0

library(tigris)

## To enable 
## caching of data, set `options(tigris_use_cache = TRUE)` in your R script or .Rprofile.

## 
## Attaching package: 'tigris'

## The following object is masked from 'package:graphics':
## 
##     plot

library(tidycensus)

1. Get an API Key from http://api.census.gov/data/key_signup.html

census_api_key("<YOUR API KEY>")
demo_variables <- # define the variables you want to analyze here
de_census_data <- get_acs(geography = "tract",
  state = "DE",
  variables = demo_variables,
  geometry = TRUE,
  cb = TRUE)

OR

1. Load de_census_data.RData

load("data/de_census_data_export.RData")

• No need to do this if you used Choice 2

de_census_data_clean <- de_census_data %>%
    separate(col = NAME, into = c("Census_Tract", "County", "State"), sep = ",") %>%
    separate(col = Census_Tract, into = c("Census", "Tract", "Number"), sep = " ") %>%
    setnames(old=c( "Number"), new=c("Census_Tract_Number"))

# Wilmington Tracts - ask Eli where to find these numbers
Wilm_census_data <- de_census_data_clean %>%
  filter(Census_Tract_Number %in% c(2, 3, 4, 5, 6.01, 6.02, 9, 11, 12, 13, 14, 15, 16, 19.02, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30.02))

# Plot all our data
ggplot(Wilm_census_data, aes(fill = estimate)) +
  geom_sf() +
  scale_fill_viridis_c() +
  scale_color_viridis_c(guide = FALSE) +
  theme_minimal() +
  coord_sf(crs = 26916, datum = NA) +
  labs(title = "Estimates by Census Tract",
       subtitle = "Wilmington, DE",
       caption = "Data source: 2017 ACS.
       \nData acquired with the R tidycensus package.",
       fill = "ACS estimate") +
  facet_wrap(~variable)

What’s an issue here?

R being vectorized allows us to use commands like:

• facet
• group_by